Neural network versus activity-specific prediction equations for energy expenditure estimation in children

Ruch, Nicole; Joss, Franziska; Jimmy, Gerda; Melzer, Katarina.; Hänggi, Johanna; Mäder, Urs (2013). Neural network versus activity-specific prediction equations for energy expenditure estimation in children Journal of Applied Physiology, 115(9), pp. 1229-1236. American Physiological Society 10.1152/japplphysiol.01443.2012

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The aim of this study was to compare the energy expenditure (EE) estimations of activity-specific prediction equations (ASPE) and of an artificial neural network (ANNEE) based on accelerometry with measured EE. Forty-three children (age: 9.8 ± 2.4 yr) performed eight different activities. They were equipped with one tri-axial accelerometer that collected data in 1-s epochs and a portable gas analyzer. The ASPE and the ANNEE were trained to estimate the EE by including accelerometry, age, gender, and weight of the participants. To provide the activity-specific information, a decision tree was trained to recognize the type of activity through accelerometer data. The ASPE were applied to the activity-type-specific data recognized by the tree (Tree-ASPE). The Tree-ASPE precisely estimated the EE of all activities except cycling [bias: −1.13 ± 1.33 metabolic equivalent (MET)] and walking (bias: 0.29 ± 0.64 MET; P < 0.05). The ANNEE overestimated the EE of stationary activities (bias: 0.31 ± 0.47 MET) and walking (bias: 0.61 ± 0.72 MET) and underestimated the EE of cycling (bias: −0.90 ± 1.18 MET; P < 0.05). Biases of EE in stationary activities (ANNEE: 0.31 ± 0.47 MET, Tree-ASPE: 0.08 ± 0.21 MET) and walking (ANNEE 0.61 ± 0.72 MET, Tree-ASPE: 0.29 ± 0.64 MET) were significantly smaller in the Tree-ASPE than in the ANNEE (P < 0.05). The Tree-ASPE was more precise in estimating the EE than the ANNEE. The use of activity-type-specific information for subsequent EE prediction equations might be a promising approach for future studies.

Item Type:	Journal Article (Original Article)
Division/Institute:	Swiss Federal Institute of Sports Magglingen SFISM > EHSM - Leistungssport Swiss Federal Institute of Sports Magglingen SFISM > EHSM - Lehre und Sportpädagogik > Monitoring
Name:	Ruch, Nicole; Joss, Franziska; Jimmy, Gerda; Melzer, Katarina.; Hänggi, Johanna and Mäder, Urs
ISSN:	8750-7587 (Print) 1522-1601 (Online)
Publisher:	American Physiological Society
Language:	English
Submitter:	Service Account
Date Deposited:	15 Feb 2021 13:38
Last Modified:	12 Oct 2021 02:18
Publisher DOI:	10.1152/japplphysiol.01443.2012
Related URLs:	Publication
PubMed ID:	23990244
Uncontrolled Keywords:	Automated pattern recognition Energy metabolism Child Physical activity Accelerometer
ARBOR DOI:	10.24451/arbor.11078
URI:	https://arbor.bfh.ch/id/eprint/11078